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Underwater Shrimp Digital image Segmentation Using Edge Detection Method on Fog Network 2

Underwater Shrimp Digital Image Segmentation Using Edge Dete...

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2nd International conference on Information Technology and Education, ICIT and E 2022

作者： Setiawan, Arif Triyanto, Wiwit Agus Setiawan, Aji Warsito, Budi Wibowo, Adi Muria Kudus University Department of Information System Kudus Indonesia Darma Persada University Department of Information Technology Jakarta Indonesia Diponegoro University Department of Statistics Semarang Indonesia Diponegoro University Department of Informatics Semarang Indonesia

ISBN: (纸本)9781665494335

This study takes the theme of underwater shrimp digital image segmentation using edge detection methods on fog networks, the problem raised in this study is to compare the accuracy of the shrimp digital image segmentation process using five edge detection methods, the parameters used are PSNR and MSE values, five methods Edge detection used in the digital image segmentation process of underwater shrimp is the Canny, Sobel, Prewitt, Roberts and Laplacian of Gaussian (LOG). This study proposes a fog network technique in the computing process by taking data through an underwater camera connected to a fog server to provide real-time information services, the study was carried out using 5 digital images of underwater shrimp extracted from video data, namely image frames to 140, 850, 1185, 5950 and 6390 which are implemented in 5 edge detection methods. from the results of the study, the best edge detection method was the canny method with the lowest PSNR accuracy rate of 4.4619 dB and the highest MSE value of 106.65. © 2022 IEEE.

关键词： image segmentation

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Virtual Staging Technologies for the Metaverse 2

Virtual Staging Technologies for the Metaverse

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2024 International conference on Intelligent Metaverse Technologies & Applications

作者： Tukur, Muhammad Boraey, Yehia Jashari, Sara Villanueva, Alberto Jaspe Shah, Uzair Al-Zubaidi, Mahmood Pintore, Giovanni Gobbetti, Enrico Schneider, Jens Agus, Marco Fetais, Noora HBKU Coll Sci & Engn Doha Qatar Qatar Univ Dept Comp Sci Doha Qatar KAUST VCCVIS High Petf Visualizat Jeddah Saudi Arabia CRS4 Visual & Data Intens Comp Cagliari Italy

ISBN: (纸本)9798350351521;9798350351514

We discuss virtual staging technologies, focusing on two primary pipelines for creating and exploring immersive indoor environments in the metavers: an AI-based image processing pipeline and a LIDAR-based pipeline. The AI-based image processing pipeline leverages advanced AI algorithms for tasks such as clutter removal, semantic style transfer, and super-resolution, enabling rapid generation of high-quality, photorealistic virtual environments from single panoramic images. The LIDAR-based pipeline captures measurable 3D models of indoor spaces, facilitating immersive editing and collaborative design through realtime interaction with high-fidelity virtual environments. A qualitative comparative analysis of these technologies highlights their strengths and limitations in various applications. The practical implications of these pipelines are discussed, particularly their potential to transform industries such as real estate, furniture retail, interior design, construction, remote collaboration, and immersive training. The paper concludes with suggestions for future research, including conducting user studies, integrating the two pipelines, and optimizing technologies for mobile and edge devices to enhance accessibility and usability.

关键词： Metaverse Virtual Staging LIDAR AI Immersive Environments Collaborative Design

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Computer Vision and Deep Learning-Based Model for Detecting Spoofed Faces in images 2nd

Computer Vision and Deep Learning-Based Model for Detectin...

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2nd International conference on data Science and Network Engineering, ICDSNE 2024

作者： Salian, Gayathri P Rao, Manasa K Rashmi, M. Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819783359

In the modern era of smart applications, video data is critically important in various contexts. In most of these applications, cameras are frequently incorporated to facilitate authentication. As a result, face recognition is the biometric method most frequently employed to authenticate users in these applications. The vulnerability of face recognition systems to spoofing attacks grows in tandem with their increased usage. As a result, robust countermeasures are required. This paper presents an approach to face anti-spoofing through transfer learning and YOLOv8 optimization. Additionally, a custom dataset was constructed using images obtained from web cameras and an existing dataset to assess the proposed work’s real-time effectiveness. The proposed approach also adds a blurriness threshold during image capture to improve performance. With a mean Average Precision (mAP50) of 0.975, the experimental outcomes highlight the model’s effectiveness in detecting face spoofing. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biometrics

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Cross-Band Fusion for Multispectral Scene Classification 2

Cross-Band Fusion for Multispectral Scene Classification

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2nd IEEE International conference on Signal, Information and data Processing, ICSIDP 2024

作者： Li, Jin Liu, Yu Jiang, Zhizhuo Wang, Xueqian Zheng, Bolun Tsinghua University Shenzhen International Graduate School Shenzhen China Tsinghua University Department of Electronic Engineering Beijing China Hangzhou Dianzi University School of Automation Hangzhou China

ISBN: (纸本)9798331515669

With the daily acquisition of vast amounts of remote sensing images by satellites, effectively utilizing these images for Earth observation has become a significant research focus. Multispectral image scene classification plays a fundamental role in advancing the understanding of remote sensing imagery. Existing methods for multispectral image scene classification do not sufficiently explore the interrelationships between different bands, which limits the performance of models in this task. Unlike these, we propose a cross-band fusion method for multispectral scene classification. Specifically, we employ two appropriate visual backbones to extract band-level visual features from different bands, followed by a cross-band fusion module to fuse the features from various bands adaptively. This approach fully explores the complementary information between different bands, achieving enhanced accuracy in multispectral scene classification. Extensive experiments on a commonly used dataset BigEarthNet validate that our proposed cross-band fusion method outperforms state-of-the-art (SOTA) methods. © 2024 IEEE.

关键词： image classification

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Carbon Stock Estimation at Scale from Aerial and Satellite imagery 2

Carbon Stock Estimation at Scale from Aerial and Satellite I...

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2nd IEEE conference on Artificial Intelligence (CAI)

作者： To, Alex Hoang Quoc Viet Pham Nguyen, Quang H. Davis, Joseph G. O'Sullivan, Barry Pan, Shan L. Nguyen, Hoang D. Univ Sydney Sydney Australia Univ Coll Cork Cork Ireland Reliable Machine Learning Grp Hanoi Vietnam Univ Coll Cork Insight Ctr Data Analyt Cork Ireland Univ New South Wales Inst Climate Risk & Response Sydney NSW Australia

ISBN: (纸本)9798350354102;9798350354096

In the ongoing efforts to mitigate climate change effect, the capability to reliably estimate forest carbon stock on a global scale is vital to support sustainable development. This entails the investigation of tree coverage from diverse forest ecosystems worldwide, necessitating a substantial volume of high-resolution images. This paper integrates a variety of remote sensing data sources, from aerial to satellite imagery, for the training and development of our AI system. Given the heterogeneous nature of these data sources, we develop a standardization method to ensure consistent image size and resolution between source platforms. Our harmonized dataset includes 86,088 training images and 21,768 validation images, each with a high resolution of 1.194 m(2) per pixel. We introduce a novel technique for tree semantic segmentation which offers a more effective alternative to traditional individual tree crown delineation for large-scale tree coverage estimation. To assess the adaptability of our AI models, we conducted experiments on a hand-annotated satellite image test set and achieved a High Vegetation IoU score of 45.73%. Building on these findings, we present an interactive web-based Geographic Information System for navigating high vegetation segmented satellite images and estimating carbon stock on a global scale.

关键词： remote sensing tree semantic segmentation aerial imagery satellite imagery domain adaptation

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TransCGAN-based Parameter Extraction Framework for SAR image Simulation 2

TransCGAN-based Parameter Extraction Framework for SAR Image...

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2nd IEEE International conference on Signal, Information and data Processing, ICSIDP 2024

作者： Deng, Sidan He, Jingfei Mao, Yongfei Zhao, Liangbo Chen, Liang Shi, Hao Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing Innovation Center Chongqing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China China Academy of Space Technology Institute of Remote Sensing Satellite Beijing China Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Chongqing Innovation Center Beijing China

ISBN: (纸本)9798331515669

Synthetic Aperture Radar (SAR) images have a wide range of applications due to their all-weather and all-day working conditions. However, SAR images with different scenarios and imaging conditions are insufficient or even rare, which is required in specific SAR image tasks. Fortunately, SAR image simulation technology can provide ample simulated images under these conditions at a low cost, addressing the scarcity of specific real SAR data. Accurate parameters are crucial for obtaining high-quality simulated images. However, it is time-consuming and labour-intensive to adjust parameters manually, and it often fails to achieve accurate simulation parameters. To tackle this problem, this paper proposes a TransCGAN-based SAR image simulation method that combines deep learning with traditional methods. By training TransCGAN, a conditional generative adversarial network (CGAN) integrated with Transformer architecture, a mapping between SAR images and simulation parameters is established. This enables the extraction of simulation parameters directly from the real SAR image, guided by the corresponding real SAR image. Ultimately, the parameters are subsequently converted into simulated SAR images via simulation software. Experimental results demonstrate that our TransCGAN-based method can effectively extract accurate simulation parameters from real SAR images, resulting in simulated images holding high similarity to real images. © 2024 IEEE.

关键词： Generative adversarial networks

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Recognition of Hybrid Digital Watermarking Techniques using Digital image Processing using Deep Learning Techniques 2

Recognition of Hybrid Digital Watermarking Techniques using ...

引用

2nd International conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Indira, Chilumala Noorullah, R.M. Balguri, Praveen Kumar Pinisetty, Sowjanya Krishna Institute of Aeronautical Engineerring Department of Computer Science and Engineering Dundigal Hyderabad India Institute of Aeronautical Engineerring Department of Aeronautical Engineering Dundigal Hyderabad India University of Electronic Science and Technology of China Institute of Fundamental and Frontier Science Chengdu China

ISBN: (纸本)9798350325799

Digital watermarking is the procedure to identify the source and creator of the data. This is a form of digital media marker such as video, audio or image format. It helps to protect the data from various external unauthorized access. This is done through embedded digital information in the carrier signal to identify ownership and copyright protection. Security and privacy concerns need to be improved due to the rapid increase in the transmission and receiving of digital data. Various sectors such as the navy, military, intelligence, and police are sending and receiving numerous data through online mode. The existing system utilizes Arnold map with QWT-DCT techniques which are unable to obtain invisibility in attack detection. To overcome the limitations of the existing system the proposed system is implemented. They are highly confidential and need to be secured and protected thus hybrid digital watermarking techniques such as nested forms of watermarking are employed. If the third parties tend to make any modification, then it is termed as an attack. The removal of digital watermarking by third parties through certain modifications are denoted through copyright protection application. For security applications and to avoid various types of attacks, Discrete wavelet Transform (DWT) and Non-sampled Contourlet Transform (NSCT) are employed. Security and privacy is obtained through the Quaternion Fourier Transform (QFT) based algorithm with human speech recognition. The pixel displacement with pixel encryption is enhanced to save the privacy of images. Digital watermarking requires security, robustness, watermarking keys and computational cost parameter. Thus to protect the data by neglecting various conventional attacks, digital watermarking techniques are implemented through deep convolutional neural networks. © 2023 IEEE.

关键词： image enhancement

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LSB based image Steganography with Password-Protected SMTP Transmission 2

LSB based Image Steganography with Password-Protected SMTP T...

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2nd IEEE Global conference on Communications and Information Technologies, GCCIT 2024

作者： Prakash, Mangali Bhanu Kumar, E. Ravi Anitha, Banavath Bansilal, Boda Mukkamall, Babu Reddy Sanjana, Gudipati Pranaya Vardhaman College Of Engineering Dept. of Information Technology Hyderabad India Krishna University Machilipatnam Dept. of Computer Application Machilipatnam India

ISBN: (纸本)9798350388916

Picture steganography plays a crucial role in secure communication by discreetly altering images for various purposes such as data authentication, privacy protection, and watermarking. However, traditional methods often lack resistance to manipulation. To address this, we propose a solution that merges automated password generation with the Least Significant Bit (LSB) approach. This method involves embedding text within images and automatically generating an encryption password to safeguard data security. By employing Simple Mail Transfer Protocol (SMTP) credentials, encrypted photos can be directly sent to recipients, significantly expediting the process. This automated approach eliminates the necessity for separate transfers of the image and decryption password, thereby enhancing simplicity and security. This new technology seamlessly integrates encryption, ensuring a trustworthy and secure communication channel. With this innovative solution, users can transmit sensitive information confidently, knowing that it is shielded from unauthorized access or manipulation. By combining the strengths of automated password generation, LSB steganography, and SMTP transmission, our approach provides a robust framework for safeguarding digital communication in various context. © 2024 IEEE.

关键词： Authentication Protocol

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An Automatic Method for Clay Minerals Extraction from Landsat 8 OLI data. A Case Study in Chi Linh City, Hai Duong Province 1

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2nd International conference on Geospatial Technologies and Earth Resources (GTER)

作者： Hung, Trinh Le Thanh, Nguyen Sach Kha, Vuong Trong Le Quy Don Tech Univ Hanoi Vietnam Hanoi Univ Min & Geol Hanoi Vietnam

ISBN: (数字)9783031204630

ISBN: (纸本)9783031204654;9783031204630;9783031204623

Landsat satellite data have been effectively used for mineral resources extraction. This paper presents an automatic method for clay minerals extraction from Landsat 8 OLI image based on band rationing and principal component analysis methods. Firstly, the Landsat 8 data is used to calculate the NIR/RED (band5/band4) and SWIR1/SWIR2 (band6/band7) band rationing images. To highlight the distribution of clay minerals, these band rationing images are further used to multiply the digital number values of NIR and SWIR2 bands, then obtaining (band5/4) x band5 and (band6/7) x band7 images. Finally, the principal component analysis (PCA) method is used to calculate the principal components, then select the 2nd principal component (PC2) to detect clay minerals by the automatic thresholding method. The results in this research can be used to provide input information for mineral exploration.

关键词： Remote sensing Clay minerals Band rationing Principal component analysis Landsat 8 Chi Linh city

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Contrastive Self-Supervised Learning: A Survey on Different Architectures 2

Contrastive Self-Supervised Learning: A Survey on Different ...

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2nd IEEE International conference on Artificial Intelligence (ICAI)

作者： Khan, Adnan AlBarri, Sarah Manzoor, Muhammad Arslan MBZUAI Dept Comp Vis Abu Dhabi U Arab Emirates MBZUAI Dept Machine Learning Abu Dhabi U Arab Emirates MBZUAI Dept Nat Language Proc Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665468961

Self-Supervised Learning (SSL) has enhanced the learning process of semantic representations from images. SSL has reduced the need for annotating or labelling the data by relying less on class labels during the training phase. SSL techniques dependent on Constrative Learning (CL) are acquiring prevalence because of their low dependency on training data labels. Different CL methods are producing state-of-the-art results on datasets which are used as the benchmarks for Supervised Learning. In this survey, we provide a review of CL-based methods including SimCLR, MoCo, BYOL, SwAV, SimTriplet and SimSiam. We compare these pipelines in terms of their accuracy on imageNet and VOC07 benchmark. BYOL propose basic yet powerful architecture to accomplish 74.30% accuracy score on image classification task. Using clustering approach SwAV outperforms other architectures by achieving 75.30% top-1 imageNet classification accuracy. In addition, we shed light on the importance of CL approaches which can maximise the use of huge amounts of data available today. At last, we report the impediments of current CL methodologies and emphasize the need of computationally efficient CL pipelines.

关键词： Self-Supervised Learning Contrastive Learning image Augmentation data Annotation

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